code: def remove_small_boxes(boxes, min_size):
      # type: (Tensor, float) -> Tensor
      """
      Remove boxes which contains at least one side smaller than min_size.
      移除宽高小于指定阈值的索引
      Arguments:
          boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) format
          min_size (float): minimum size
  
      Returns:
          keep (Tensor[K]): indices of the boxes that have both sides
              larger than min_size
      """
      ws, hs = (
          boxes[:, 2] - boxes[:, 0],
          boxes[:, 3] - boxes[:, 1],
      )  # 预测boxes的宽和高
      # keep = (ws >= min_size) & (hs >= min_size)  # 当满足宽，高都大于给定阈值时为True
      keep = torch.logical_and(
          torch.ge(ws, min_size), torch.ge(hs, min_size)
      )
      # nonzero(): Returns a tensor containing the indices of all non-zero elements of input
      # keep = keep.nonzero().squeeze(1)
      keep = torch.where(keep)[0]
      return keep
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/mask_rcnn/network_files/boxes.py
imports: import torch
  from typing import Tuple
  from torch import Tensor
  import torchvision
label_desc: Use image node_id 3 for calling a global function with example usage: remove_small_boxes(boxes, min_size) and returns: keep